Often in real war-time scenarios, an operator of an air or ground based vehicle uses imaging equipment in order to assess threat and make strategic decisions. A significant problem in this field is often the quality of such visual images and more specifically the problem of helping to distinguish objects that may appear in a visual image when the vehicle operator has to make a decision about the content of the picture in a short amount of time.
The present invention solves the problem of distinguishing objects that may appear in a visual image of poor quality and where an operator has to make a decision about the content of such image in a short amount of time. To better understand the decision-making process of interest, a simple military example will be first discussed. In the military application, the effects of being too aggressive or too conservative in decision-making have both costs and risks. For an example of a military application related to this scenario, assume a decision maker approaches a tree in a foreign country with a person hiding in the tree. The person in the tree may be either friendly or hostile. An immediate action of the decision-maker is required. If the decision-making process is too aggressive, the soldier on the ground will shoot at the object in the tree. If the soldier in the tree is friendly (not hostile), this “friendly fire” incident has a great penalty to the decision-maker. On the other hand, if the object in the tree is a hostile (enemy) soldier, the conservative decision to not fire at the object may result in the enemy soldier attacking the decision-maker. Thus the error in ignoring the information is even more costly to the person making the action who is required to elicit a binary choice response.
FIGS. 4a and 4b describe, in a statistical manner, the two types of errors that could occur for our military example of the identification of the man-in-the-tree just presented. In FIGS. 4a and 4b the x-axis at 401 represents decision making and the y-axis at 400 represents the probability of the intention of the man in the tree. In FIGS. 4a and 4b, the hypothesis H0 is the true situation that the man in the tree is friendly, illustrated at 402 and 404. H1 is the true situation that the man in the tree is hostile. Let us define type 1 error (friendly fire) as the event of shooting the object in the tree when it is really friendly. In FIG. 4a, this corresponds to the area A1 at 407. In FIG. 4a, we define the type 2 error (mistake of not firing at the tree) as the situation that the man in the tree is hostile but since we do not fire, the hostile agent attacks us and this is represented as area A2 at 406. The ideal situation in decision making is to minimize both the type 1 and type 2 errors at the same time.
FIG. 4a shows the types of error that exist for a binary decision-making process. Usually the decision maker operates on some measurement on the x-axis. This may be translated into the terms “don't shoot until you see the whites of their eyes”. In other words, some measurement is made on the x-axis of a variable in the environment and then an action has to be decided as belonging to the class H0 or H1 based on the measurement. The decision maker may be more or less aggressive depending on the measurement on the x-axis before selecting either H0 or H1. Thus there is a trade off between the type 1 and type 2 error thus described. The total error does not actually decrease, it just trades off (e.g. if you want less type 1 error, we absorb more type 2 error and conversely). A significant aspect of the present invention is introducing a different approach to this basic problem of decision making.
It has been documented in prior works that for certain images, by adding small amounts of noise, it is possible to enhance the recognition of specific objects in the picture. What is significant is how the noise was added. FIG. 7 shows a drawing illustrating how stochastic noise can enhance a visual image. In FIG. 7, this classic example simply added white Gaussian noise in going from left 700 to 701 and right 702. There appears to be an optimum amount of noise 701 that enables better object identification. Too little 700 or too much 702 noise is counterproductive in the identification of the object in the picture. This leads into a significant novel aspect of the present invention and solves the problem in the prior art. In the prior art, random errors are added to a visual image in a manner that object identification is improved. The present invention discloses a systematic procedure to visually enhance images by adjusting darkness levels within each primary color to embellish the recognition of distinct objects which may appear in the visual rendering.